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This work formalizes causal regions as polyhedral regions with fixed causal sets and derive upper and lower bounds on the maximal number of causal regions in both shallow and multilayer feedforward spiking networks.
Penalizing radial inflation in neural networks can accelerate algorithmic generalization by up to 6x, transforming how we approach training dynamics.
